CN102413760B - Monitoring peripheral decoupling - Google Patents

Abstract

The present invention describes a kind of for the arterial pressure decoupling of Surveillance center to periphery, the i.e. method of high power situation.These methods comprise the parameter comparing and calculate from multivariate statistical model, and this model wherein can generative center set up to the person under inspection of the high power situation of the decoupling of periphery for the person under inspection of experience normal hemodynamic situation and experience.Difference between the parameter using two multivariate statistical model to calculate or than the continuous representation provided for decoupling level, and indicate the peripheral decoupling when exceeding threshold value.These methods can be used to warn user person under inspection to experience the fact of peripheral decoupling, and provide arterial tone accurately to measure, and this makes other parameter such as stroke volume and kinemic exact value calculate becomes possibility.

Description

Monitoring peripheral decoupling

Background technology

Indicator especially such as stroke volume (stroke volume) (SV), cardiac output (cardiacoutput) (CO), end diastolic volume (end diastolic volume), ejection fraction (ejectionfraction), stroke volume variation (SVV), pulse pressure variation (PPV) and systolic pressure variation (SPV) etc. is not only important to medical diagnosis on disease, and it is important for also namely monitoring the clinical remarkable variation of person under inspection continuously to " in real time ".Such as, the variation of care provider to the preloading dependency of humans and animals person under inspection, fluidic response or volume response is interested.Therefore few hospital does not have the equipment of some forms to monitor one or more heart indicator, thus attempt provides one or more in the esoteric alarm of person under inspection in the variation of expression.Comprise invasive technique, many technology of noninvasive technology and combination thereof are used and even more technology are suggested in the literature.

Summary of the invention

The method of monitoring person under inspection Ti Nei center to peripheral arterial pressure decoupling is described.These methods comprise provides arterial pressure waveform data and to arterial pressure waveform market demand first (decoupling) multivariate statistical model from person under inspection, thus determines and provide decoupling arterial tone (arterialtone) value of person under inspection.First (decoupling) multivariate statistical model is from being derived from the one group arterial pressure waveform data preparation of experience center to a group test person under inspection of peripheral arterial pressure decoupling.Then second (normally) multivariate statistical model is applied to arterial pressure waveform data, thus determines and provide the natural arterial tension value of person under inspection.Second (normally) multivariate statistical model is prepared from one group of arterial pressure waveform data of a group test person under inspection with normal hemodynamic situation.Once calculate decoupling and the natural arterial anxiety of person under inspection, then compare these values.Difference between first arterial tone of person under inspection and second arterial tone of person under inspection is greater than threshold value and represents that person under inspection experiences center to peripheral arterial pressure decoupling.Similarly, the ratio of arterial tone to second arterial tone of person under inspection of person under inspection is greater than threshold value than representing that person under inspection experiences center to peripheral arterial pressure decoupling.

Accompanying drawing explanation

Fig. 1 illustrates during normal hemodynamic situation, the pressure waveform simultaneously recorded in ascending aorta (ascending aorta) (aortal), femoral artery (stock) and radial artery (oar) in the animal model of pig.

Fig. 2 illustrates and is recovering endotoxin shock (septic shock) period, the pressure waveform simultaneously recorded in ascending aorta (aortal), femoral artery (stock) and radial artery (oar) in the animal model of pig with a large amount of fluid and vasopressor.

Fig. 3 illustrates at an example by the complicated blood pressure curve of cardiac cycle of fighting.

Fig. 4 illustrates that the discrete time of the pressure waveform of Fig. 3 characterizes.

Fig. 5 illustrates the region below the constriction of arterial pressure waveform.

Fig. 6 illustrates the statistical distribution of the contracted state lower zone of the arterial pressure waveform normal person under inspection and Gao power person under inspection.

During Fig. 7 illustrates that arterial pressure waveform shrinks.

Fig. 8 illustrates the statistical distribution during the contraction of the arterial pressure waveform of normal person under inspection and Gao power person under inspection.

During Fig. 9 illustrates the contraction of arterial pressure waveform and between relaxing period.

Figure 10 is in normal hemodynamic situation (dotted line) and high power situation (thick line), the statistical distribution during the diastole state of high heart rate person under inspection, in conjunction with the distribution of all patients be also illustrated (fine rule).

Figure 11 is in normal hemodynamic situation (dotted line) and high power situation (thick line), the statistical distribution during the contracted state of high heart rate person under inspection, in conjunction with the distribution of all patients be also illustrated (fine rule).

Figure 12 illustrates the block diagram implemented at the primary clustering of the system of this describing method.

Detailed description of the invention

Surveillance center is described to peripheral arterial pressure decoupling, i.e. the method for high power situation.These methods comprise the arterial tone comparing and calculate from multivariate statistical model, and described model is for the person under inspection for the normal hemodynamic condition of experience and the person under inspection that can experience high power situation between the emergence period to peripheral decoupling at center set up.Difference between the arterial tone using two multivariate statistical model to calculate can be used to the Radinal pressure decoupling represented when exceeding threshold value.These methods warning user person under inspection experiences the fact of center to peripheral decoupling, and provides arterial tone accurately to measure, and it makes stroke volume and kinemic accurate calculating become possibility, and it enables clinician provide treatment to person under inspection is suitable successively.

As used herein, term height power and be vasodilatively meant to wherein peripheral arterial pressure and the flowing situation from central aortic pressure and flowing decoupling, and the tremulous pulse be meant to away from heart location of term peripheral arterial, such as radial artery, femoral artery or brachial artery.The normal relation that decoupling arterial pressure is meant between peripheral arterial pressure and central aortic pressure is invalid, and peripheral arterial pressure should not be used to determine central aortic pressure.It is proportional with central aortic pressure or be not the situation of its function that this does not comprise wherein peripheral arterial pressure yet.In normal hemodynamic situation, along with more measuring away from heart, then blood pressure increases.Such pressure increases shown in Figure 1, and the pressure wave amplitude namely measured at radial artery is greater than the pressure measured at femoral artery, the latter and then be greater than aortic pressure.The difference of these pressure relates to wave reflection, and namely pressure amplifies to periphery.

This normal hemodynamic relation of pressure, the pressure namely away from heart increases, and often relies on medical diagnosis.But in high power/vasodilation situation, this relation can become and put upside down, and namely arterial pressure becomes lower than center main arterial pressure.This puts upside down owing to the arterial tone in such as peripheral blood vessel, and it is considered to affect above-mentioned wave reflection.High power situation is like this shown in Figure 2, the pressure wave amplitude namely measured at radial artery lower than the pressure measured at femoral artery, the latter and then lower than aortic pressure.Think and make the medicine of little peripheral arterial diastole (such as, nitrate, ACE inhibitor and calcium inhibitors) facilitate high power situation.The serious vascular diastole situation of these types is also often just observed in situation after cardiopulmonary bypass art (cononary artery bypass), and wherein pressure of the radial artery underestimates the pressure in aorta.Wherein the peripheral arterial pressure basic center of underestimating center main arterial pressure to the pressure differential of periphery usually causing observing in the patient body with Severe sepsis of serious vascular diastole with a large amount of fluid and the treatment of high dose vasopressor.Closely similar situation is also observed in the patient body with end-stage liver disease.As those skilled in the art well recognize, in normal hemodynamic situation, some treatment of person under inspection is close to the person under inspection be different from high power situation.Therefore, method of the present disclosure detects the vasodilation (as existed) in patient body and also provides the suitable calculating based on arterial tone.

In the high power of measurement described here and not high power person under inspection body, the method for arterial tone generally includes the step providing arterial pressure waveform data from person under inspection, then analyzes the step of these data.First, analyze the arterial pressure waveform of person under inspection thus determine the decoupling arterial tone of person under inspection.Next, analyze the arterial pressure waveform of person under inspection thus determine that the natural arterial of person under inspection is nervous.These step serializables (with any order) or executed in parallel.Then, the decoupling arterial tone and the natural arterial that compare person under inspection are nervous.Difference between the decoupling arterial tone of person under inspection and natural arterial anxiety is greater than threshold value and represents that person under inspection experiences center to peripheral arterial pressure decoupling.Similarly, the ratio of decoupling arterial tone to the natural arterial anxiety of person under inspection of person under inspection is greater than threshold value and represents that person under inspection experiences center to peripheral arterial pressure decoupling.

In these methods, determine whether the peripheral arterial pressure of person under inspection comprises to arterial pressure waveform market demand multivariate statistical model from the central aortic pressure decoupling of person under inspection.First (decoupling) multivariate statistical model is prepared from first group of arterial pressure waveform data of the first crowd of test person under inspection being derived from decoupling between experience peripheral arterial pressure and central aortic pressure.Second (normally) multivariate statistical model is from the second group of arterial pressure waveform data preparation being derived from the second crowd of test person under inspection not experiencing decoupling between peripheral arterial pressure and central aortic pressure.Each multivariate statistical model provides the arterial tone value about two test person under inspection groups.

Multivariate statistical model based on organizing factor more as used herein, comprises the one or more parameters that the blood vessel situation by person under inspection affects.The factor of each type used, such as beat pulse standard deviation, expresses the difference between the person under inspection of the special blood vessel situation of experience and the person under inspection not experiencing this situation usually.But, the frequent consecutive tracking of this difference, and concrete person under inspection can have clear and definite decoupling represent and clear and definite normal represent between value, even if or still can occur within normal range because some reasons person under inspection of person under inspection experiences this concrete factor of blood vessel situation.But, by using multiple factor, namely by multiple factors that blood vessel situation affects, usually having and enough positive representing thus represent to there is situation (or have enough negative represent thus represent to there is not situation).Multivariate statistical model as described in this provides and uses multiple factor thus the ability increasing the arterial tone accurately calculating two states (namely experience or do not experience peripheral decoupling).

Concrete quantity for the factor of multivariate statistical model depends on that single factor is experiencing the person under inspection of concrete condition and do not experiencing the ability distinguished between the person under inspection of this concrete condition.The quantity of factor also can increase thus provide the accuracy of larger level to model.Therefore, according to the needs in specific environment, the factor of larger quantity can be used to contribute to the precision of model, accuracy and/or repdocutbility.The example being used in the factor of this descriptive model comprises (a) parameter based on the standard deviation of arterial pressure waveform data, b () is based on the parameter of person under inspection's heart rate, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure dependence Windkessel compliance of one group of arterial pressure waveform data, n () is based on the parameter of person under inspection's body surface area.Other factors that can use together with multivariate statistical model described here comprise (o) based on by fighting arterial blood pressure signal shape and have the parameter of at least one statistical moment of arterial blood pressure signal of one or larger order, and lineup's bulk measurement parameter of (p) person under inspection.One or more (or in these factors whole) in these factors can be used for multivariate statistical model described here.

Factor for multivariate statistical model described here calculates from based on the signal of arteriotony or the signal proportional with arteriotony.The calculating of cardio-vascular parameters such as arterial compliance (arterial tone) is at U.S. Patent Application Serial Number No.10/890, and describe in 887, this application is filed on July 14th, 2004, and its full content is included in this reference.Be described below for calculating cardio-vascular parameters, so that the example of the factor used together with method disclosed herein and data, this cardio-vascular parameters is included in U.S. Patent Application Serial Number No.10/890, the parameter discussed in 887.

Fig. 3 is the example of arterial pressure waveform P (t) gathered at single cardiac cycle.At time t _dia0this cardiac cycle is at diastole pressure point P _diastart, by arriving systolic pressure P _systime t _systhe time of advent t _dia1, again reach P at this time blood pressure _dia.

Signal useful together with this method comprises the cardio-vascular parameters based on arteriotony or any signal proportional with arteriotony, and it is at arterial tree, and such as, any some invasive in radial artery, femoral artery or brachial artery or Noninvasive place are measured.As used herein, term arterial pressure waveform data are meant to based on the data of arteriotony or the data based on any signal proportional with arteriotony.If use the pressure converter that invasive instrument, particularly conduit are installed, so any tremulous pulse is all possible measurement point.The placement of Noninvasive changer usually by instrument self domination, such as, points cuff, upper arm pressure cuff and ear lobe clamp.Have nothing to do in the concrete instrument used, the data of acquisition finally produce the signal of telecommunication of corresponding with arteriotony (such as, proportional).

As diagram in the diagram, analogue signal such as arteriotony can use any standard module transducer (ADC) to be digitized as series of values.I.e. t ₀≤ t≤t _farteriotony known method and circuit can be used to be converted into digital form P (k), k=0, (n-1), wherein t ₀and t _fbe measure the initial of interval and final time, and n comprise in the calculation, usually in the quantity measuring the arteriotony sample that interval is evenly distributed.

For catching related data from such numeral or digitized signal, consider the ordered collection of m value, i.e. sequence Y (i), wherein i=1 ..., (m-1).As well understood from statistics field, four square μ of Y (i) ₁, μ ₂, μ ₃and μ ₄known formula can be used to calculate, wherein μ ₁meansigma methods (i.e. arithmetic average), μ ₂=σ ²be deteriorated (i.e. standard deviation sigma square), μ ₃the degree of bias, and μ ₄it is kurtosis.Therefore:

μ ₁=Y _avg=1/m* (∑ Y (i)) (formula 1)

μ ₂=σ ²=1/ (m-1) * ∑ (Y (i)-Y _avg) ²(formula 2)

μ ₃=1/ (m-1) * ∑ [(Y (i)-Y _avg)/σ] ³(formula 3)

μ ₄=σ/(m-1) * ∑ [(Y (i)-Y _avg)/σ] ⁴(formula 4)

Usually, β square μ _pcan be expressed as:

μ _β=1 (m-1) * 1/ σ ^β* ∑ [(Y (i)-Y _avg)/σ] ^β(formula 5)

Wherein i=0 ..., (m-1).For well-known statistics reason, the centrifugal pump formula of the second to the four square is usually by being multiplied by 1/ (m-1) instead of 1/m.

The available factor of method described here is not only the function of pressure waveform P (k) four squares, is also the function of the time arrow of pressure weighting.Standard deviation sigma provides the shape information of a level, and wherein σ is larger, then more " scattering ", namely function Y (i) more trends towards deviating from meansigma methods function Y (i).Although standard deviation provides some shape informations, but by considering its shortcoming of content easy understand below: if the order that its intermediate value forms sequence Y (i) " is put upside down ", namely Y (i) is shifted around i=0 axle mirror image, to be worth Y (m-1) becoming the first value in time, so meansigma methods and standard deviation do not change.

The degree of bias is the measured value that symmetry lacks, and whether the left side of representative function Y (i) or right side overweight opposite side relative to statistical model.Positive partial function rises rapidly, reaches its peak value, then slowly declines.Negative bias function is contrary.Problem is that degree of bias value comprises the shape information do not found in meansigma methods or standard deviation, and particularly, how degree of bias value representative function promptly initially rises to its peak value, then its how slow-decay.Two different functions can have same average value and standard deviation, but then they only seldom have the identical degree of bias.

Kurtosis is whether more function Y (i) compares normal distribution spike or more smooth measured value.Therefore, peak angle value represents the obvious peak value close to meansigma methods, and is after this declining, succeeded by heavy " afterbody ".Ebb angle value trends towards the region relatively flat of representative function at its peak value.Normal distribution has the kurtosis of 3.0; Therefore actual kurtosis value often adjusts 3.0, and therefore this value substitutes initial point.

Use by four statistical moments of arterial pressure waveform of fighting advantage for square be accurate and sensitive mathematics measurement by arterial pressure waveform of fighting.Because arterial compliance and Peripheral resistance directly affect the shape of arterial pressure waveform, therefore by measuring the effect directly assessing arterial compliance and Peripheral resistance by the shape of arterial pressure waveform of fighting.The shape sensitive statistical moment by arterial pressure waveform of fighting together with other arterial pressure parameter described here can effectively be used for measuring vascular compliance and Peripheral resistance in conjunction with effect, i.e. arterial tone.Arterial tone performance arterial compliance and Peripheral resistance in conjunction with effect, and the impedance of the 2-element electrical analogue equivalent model of corresponding well-known Windkessel hemodynamics model, this model is made up of electric capacity and resistive component.By measuring arterial tone, based on other parameters some of arterial tone, such as arterial elasticity, stroke volume and cardiac output also can be directly measured.Arbitrary parameter in method described here in these parameters all can be used as factor.

At front four square μ of pressure waveform P (k) _1P, μ _2P, μ _3Pand μ _4Pcalculated and for multivariate Boolean or multivariate statistical model time, wherein μ _1Pmeansigma methods, μ _2Pp=σ _p ²be deteriorated, i.e. standard deviation sigma _psquare; μ _3Pthe degree of bias, and μ _4Pbe kurtosis, wherein these squares are all based on pressure waveform P (k).Formula 1-4 above can be used to substitute into after Y, k substitute into i and n substitution m at P calculate these values.

Formula 2 above provides " textbook " method calculating standard deviation.Other more appropriate method also can use.Such as, at least based under the background of blood pressure measurement, to σ _prough approximation be difference between minimum and maximum measured pressure value divided by three, and about the maximum of P (t) first derivative of time or minima absolute value usually and σ _pproportional.

As Fig. 4 diagram, at each discrete time k, corresponding measurement pressure is P (k).Value k and P (k) can be formed as corresponding histogrammic sequence T (j), is meant to " counting " that each P (k) value is used as corresponding k value.The example such as greatly simplified, supposes that whole pressure waveform is only made up of four measured value P (1)=25, P (2)=50, P (3)=55 and P (4)=35.Then, this can show as the sequence T (j) with 25,50 two, 55 three and 35 four:

T (j)=1,1 ..., 1,2,2 ..., 2,3,3 ..., 3,4,4 ..., therefore 4 these sequences have 25+50+55+35=165 item.

As for other sequence any, square can be calculated for this sequence.Such as, meansigma methods (the first square) is:

μ _1T=(1*25+2*50+3*55+4*35)/165=430/165=2.606 (formula 6) and standard deviation sigma _tvariation μ _2Tsquare root:

SQRT[1/164*25(1-2.61) ²+50(2-2.61) ²+55(3-2.61) ²+35(4-2.61) ²]＝0.985

Degree of bias μ _3Twith kurtosis μ _4Tcalculate by the similar substitution in formula 3 with 4:

μ _3T={ 1/ (164) * (1/ σ _t ³) ∑ [(P (k) * (k-μ _1T) ³] (formula 7)

μ _4T={ 1/ (164) * (1/ σ _t ⁴) ∑ [(P (k) * (k-μ _1T) ⁴] (formula 8) wherein k=1 ..., (m-1).

As these formula represent, in fact this process made each its corresponding pressure value P (k) of discrete time value k " weighting " before the square of computation time.This sequence T (j) has very useful character, and it robustly characterizes the timing distribution of pressure waveform.Under the almost whole circumstances, the order of pressure value P (k) is put upside down will even cause the meansigma methods of T (j) to change, and the variation of all higher order squares.In addition, usually at secondary " hump " also appreciable impact kurtosis μ that dicrotism pressure (dicrotic pressure) Pdicrotic occurs _4Tvalue; On the contrary, in Romano method, such as simply identify that dicrotic notch (dicrotic notch) needs to have noise calculation at least one derivative in prior art.

Due to the very accurate measurements that pressure weighting square is amplitude by Signal for Pulse of beating and temporal information, therefore it provides the shape information of another level by Signal for Pulse of beating.Use except the external pressure weighting square of pressure waveform square also can increase the accuracy of model described here.

A cardio-vascular parameters useful together with method described here is arterial tone factor χ, but it self is used as cardio-vascular parameters, or for other cardio-vascular parameters such as stroke volume or kinemic calculating.The calculating of arterial tone χ can use whole four of such as pressure waveform and pressure weight temporal square.Other parameter comprises in the calculation, thus considers other known features, the concrete complicated pattern of patient of such as vessel branch.The example be worth in addition comprises heart rate HR (or R wave period), other somatometry parameter of body surface area BSA or person under inspection, use the method (" The Static Elastic Propertiesof 45 Human Thoracic and 20 Abnormal Aortas in vitro and the Parametersof a New Model " that the people such as known method such as Langewouters describe, J.Biomechanics, 17 (6): 425-435 (1984)) the compliance value C (P) calculated, wherein the method calculates compliance as pressure wave and patient age and sex, based on arterial blood pressure signal shape and the parameter of at least one statistical moment of arterial blood pressure signal with one or larger order, based on the parameter of area below the constriction of arterial blood pressure signal, based on the parameter of duration of contraction and based on the polynomial function of duration of contraction to the parameter of the ratio of diastole persistent period.

These last three cardio-vascular parameters, area below the constriction of i.e. arterial blood pressure signal, shrink the ratio continuing and shrink and continue to continue diastole, can affect by arterial tone and blood vessel adaptability, and therefore change, such as change between the person under inspection and the person under inspection of Gao power situation of normal hemodynamic situation.Because these three cardio-vascular parameters change between normal and high power person under inspection, so method described here can use these cardio-vascular parameters to detect vasodilation in person under inspection peripheral arterial or vasoconstriction.

Figure illustrates the area (Asys) below arterial pressure waveform constriction in Figure 5.Area below arterial pressure signal medium-sized artery corrugating constriction is defined as waveform to start and terminates in dicrotism recess (from a b to the area of beneath portions d) on Fig. 5 from heart beating.The energy of arterial pressure signal during the area performance of shrinking below is shunk, it is directly proportional to stroke volume, and is inversely proportional to arterial compliance.When the group of normal and high power patient measures, A can be detected _sysin displacement.As shown in fig. 6, during shrinking, the energy of arterial pressure signal is higher, such as, in some the person under inspection's bodies in high power situation.There is higher A _systhese persons under inspection normally there is the person under inspection of high cardiac output (CO) and low or normal HR, the CO wherein improved caused mainly through the cardiac contractility improved, this means that these persons under inspection have the stroke volume of increase and the arterial compliance of minimizing, during it is directly reflected in contraction arterial pressure signal energy in.During during many high power situations, usual echo strongly also significantly can facilitate contraction, the energy of signal increases.

Persistent period (the t shunk _sys) in the figure 7 figure illustrate.The persistent period of shrinking in arterial pressure waveform to be defined as from heart beating to dicrotism recess (from a b to the persistent period d) on Fig. 7.The persistent period of shrinking directly affects by arterial compliance, and is relatively independent of the variation of peripheral arterial anxiety, only when large echo exists.(data are to higher t higher than the duration of contraction in normal person under inspection's body for such as, duration of contraction as illustrated on Fig. 8, in some high power person under inspection bodies _sysvalue displacement).As visible for shrinking energy, in the patient body with high CO equally with low or normal HR, duration of contraction is usually higher, the CO wherein improved caused mainly through the cardiac contractility improved, and wherein contractility can not be enough high so that increase contraction energy not.Increase stroke volume in these patient bodies is partly the contractility owing to increasing, and is partly the duration of contraction owing to increasing.Echo here also works.

The further parameter such as changed between normal and high power person under inspection is duration of contraction (t _sys) to diastole persistent period (t _dia) ratio, as figure illustrates in fig .9.In arterial pressure waveform, the persistent period of diastole is defined as terminating from dicrotism recess to cardiac cycle (from a d to the persistent period e) on Fig. 9.In some high power situations, the ratio of contraction and diastole persistent period is significantly higher than the ratio observed in normal hemodynamic situation.This observes usually in the patients with septic shock body of CO with raising, and wherein HR is also high.In these types of cases, shrink and take almost all cardiac cycles, only before next cardiac cycle starts for diastole leaves the considerably less time.This is shown in Figure 10 and Figure 11, and this illustrates with in normal patient body in patients with septic shock body, the persistent period (Figure 10) of diastole during high HR situation and the persistent period (Figure 11) of contraction.As illustrated in the drawings; high HR patient (dotted line) in normal hemodynamic situation trends towards having lower shrinkage and diastole persistent period; and the high HR patient (solid line) of septic shock trends towards having the low diastole persistent period, but there is normal or high duration of contraction.

Based on other parameter of arterial tone factor, such as stroke volume (SV), cardiac output (CO), arterial flow or arterial elasticity can be used as the factor in method described here.Such as, the product that stroke volume (SV) can be used as arterial tone and arterial pressure signal standard deviation is calculated:

SV=χ * σ _p(formula 9)

Wherein:

SV is stroke volume;

X is arterial tone; And

σ _pit is the standard deviation of arterial pressure.

Analogue measurement interval, the i.e. time window [t that implements at this time window of each given period ₀, t _f] and therefore discrete sampling interval k=0 ..., (n-1) is enough little, and therefore it does not comprise substantial displacement in pressure and/or time square.But the time window longer than cardiac cycle provides suitable data.Preferably, measuring interval is multiple cardiac cycles that the identical point in different cardiac cycle starts and terminates.The average pressure value calculated for various higher order square will use average pressure value P to use multiple cardiac cycle to guarantee _avg, it can not produce deviation because of the imperfect measurement in cycle.

Larger sampling window tool has the following advantages, and namely usually reduces the effect of disturbance, such as, by reflecting the effect of the disturbance caused.Normal experiment well known to the skilled person and clinical method determination right times window can be used.Attention time window is consistent with single cardiac cycle is possible, and average pressure displacement is nonsensical in the case.

Time window [t ₀, t _f] also according to P _avgdrift adjustable.Such as, if at the P of window preset time _avgwith the P of previous time window _avgcomplete difference or proportional difference, more than threshold quantity, so can reduce time window; P in the case _avgdegree of stability to be then used for express time window expansible.Time window also or can be expanded based on snr measurement or variation and reduce based on noise source.Restriction is preferably paid close attention to and is allowed time window expansion or reduce how many, and if allow such expansion completely or reduce, so the expression of interval shows preferably to user.

Time window does not need any special point started from heart beat cycle.Therefore, t ₀do not need and t _dia0identical, although this can be select easily in much enforcement.Therefore, the beginning at each measurement interval and end (i.e. t ₀and t _f) can be triggered, such as, at time t according to almost any characteristic of heart beat cycle _dia0or t _sys, or be triggered according to non-pressure characteristic such as R ripple etc.

Any other input signal proportional with blood pressure can be used, instead of direct Measure blood pressure.This means to realize calibration in any or all of some place in some points in the calculation.Such as, if the signal outside arteriotony self is used as input, so it can be calibrated to blood pressure before its value is used for calculating various component square, or had been calibrated afterwards in the amendable situation of square value produced.In brief, cardio-vascular parameters can use in some cases and be different from this fact of input signal that arteriotony directly measures and do not get rid of it and generate the ability that accurate compliance estimates.

Create multivariate model thus calculate arterial tone and comprise some steps.Such as, Multilinear Regression Response surface meth od can be used to set up this model.The quantity of the item used in a model can use some numerical value approach to be determined, thus the mean square error between minimum model output valve and the determined arterial tone value of alternative method passing through this model of pressure.Particularly, multinomial multivariable fitting function is used for generating the multinomial coefficient for often organizing arterial pressure waveform parameter and give χ value, as follows:

$x=\left[\begin{array}{cccc}{a}_1& a_2& ...& a_n\end{array}\right]*\left[\begin{array}{c}{x}_1\\ x_2\\ ...\\ x_n\end{array}\right]$ (formula 10)

Wherein a ₁... a _nthe coefficient of multinomial multivariate regression models, and x ₁... x _nit is the predictor variable of model.Predictor variable is selected from the factor discussed above deriving from arterial pressure waveform.

The predictor variable χ of model _iin each be arterial pressure waveform parameter v _ipredetermined combinations, and can to calculate as follows:

(formula 11)

Coefficient v _idifferent time and the frequency domain parameter of arterial pressure waveform.

Such as, 11 arterial pressure waveform parameters are used to create multivariate statistical model.These parameters are: v ₁(standard deviation (the σ of arterial pressure _p)), v ₂(heart rate), v ₃(mean arterial pressure (P _avg)), v ₄(pressure weighting standard difference (σ _t)), v ₅(pressure weighting MAP (μ _1T)), v ₆(the degree of bias (the μ of arterial pressure _3P)), v ₇((kurtosis (the μ of arterial pressure _4P)), v ₈(the pressure weighting degree of bias (μ _3T)), v ₉(pressure weighting kurtosis (μ _4T)), v ₁₀(Windkessel compliance (the C of pressure dependence _w)) and v ₁₁(patient body surface areas (BSA)).Coefficient a _idetermine by using the multivariate least square regression of the factor data of collecting from person under inspection with exponential matrix " P ".Coefficient and exponential factor relate to by thermodilution method for many "True" stroke volumes determined with reference to person under inspection.In the model, A and B sets up as follows:

A＝2.95-0.43472 12.384-143.49 21.396-1.3508 0.029824-7.3862

$P=\left[\begin{array}{ccccccccccc}0& 0& 0& 0& 0& 0& 0& 0& 0& 0& 1\\ 0& 0& 0& 0& 0& 0& 0& 0& 0& -1& 0\\ 0& 0& 2& 0& 0& 0& 0& 0& 0& 0& -2\\ 0& -2& 1& 0& 0& 0& 0& 0& 0& 0& 0\\ 0& -2& 0& 0& 0& 0& 0& 0& 0& -1& 0\\ 1& 0& 0& 0& 0& 0& -1& 0& 0& 0& 0\\ -2& 0& 0& 0& 0& 0& 0& 0& 2& 0& 0\\ 0& -1& 0& 0& 0& 0& 0& 0& 0& -1& -2\end{array}\right]$

The mode that recurrence is less than three with the parameters number retraining every regression variable performs, and wherein each parameter has the order being not more than two.Therefore, every provisional capital of matrix P has three nonzero terms at the most, and wherein the absolute value of each element of P is at most two.These constraints are used for setting up numerical stability degree and accuracy.Therefore the expression formula of χ becomes the curve of order 2 in ten one dimension parameter spaces.The multi-term expression determined for χ can write out as follows:

$x=2.95\·\mathrm{BSA}-0.43472\·\frac{1}{100\·C_W}+12.384\·\frac{{\left(\frac{1}{100}{P}_{\mathrm{avg}}\right)}^2}{{\mathrm{BSA}}^2}-143.49\·$

$\frac{\frac{1}{100}{P}_{\mathrm{avg}}}{{(10\·\frac{1}{\mathrm{HR}})}^2}+21.396\·\frac{1}{100\·C_w{(10\·\frac{1}{\mathrm{HR}})}^2}-1.3508\·\frac{\frac{1}{10}{\mathrm{\σ}}_P}{{\mathrm{\μ}}_{4P}+3}+0.029824\·$

$\frac{{({\mathrm{\μ}}_{4P}+3)}^2}{{\left(\frac{1}{10}{\mathrm{\σ}}_P\right)}^2}-7.3862\·\frac{1}{10\·\frac{1}{\mathrm{HR}}\·100\·C_W\·{\mathrm{BSA}}^2}$

Therefore, (namely first the arterial tone of person under inspection by generating model as just now described, approximating function is determined from having the person under inspection of normal hemodynamic situation or the experience center person under inspection's (depending on model) to peripheral arterial pressure decoupling, it depends on one group of the blood pressure parameter of the arterial tone clinical reference measurement obtained about performance, approximating function is the one or more function in above-described parameter, and one group of clinical reference measurement determined shows the blood pressure parameter depending on arterial tone) determine.Next, determine one group of arterial blood pressure parameter from arterial blood pressure waveform data, this group arterial pressure parameter comprises and the identical parameters being used for generating multivariate statistical model.Then by with this group arterial blood pressure parameter estimation approximating function, estimate that the natural arterial of person under inspection is nervous.

Once the multivariate statistical model of decoupling and normal condition is set up as described above, model so described here and method can be used to decoupling and the natural arterial anxiety of Continuous plus person under inspection, and the difference monitored in time or ratio variation.Difference can be simple delta (Δ) value between arterial tone, or can such as be characterized as being percent difference or variation.Similarly, than being ratio between the decoupling of person under inspection and natural arterial anxiety.Have nothing to do in the numerical value recording difference or ratio between two arterial tones as calculated, the variation exceeding this value of predetermined threshold can be used to represent that person under inspection experiences center to peripheral arterial decoupling.Further, the value such as cardiac output calculated from arterial tone also can be calculated, and these differences are monitored.As object lesson, the difference between first arterial tone and second arterial tone of person under inspection of person under inspection can be calculated as the percentage change of person under inspection first arterial tone compared with second arterial tone of person under inspection.Use the threshold value example of percentage change comprise 1% or larger, 2% or larger, 3% or larger, 4% or larger, 5% or larger, 6% or larger, 7% or larger, 8% or larger, 9% or larger, 10% or larger, 15% or larger, 20% or larger, 30% or larger, 40% or larger and 50% or larger.Similarly, use the threshold value example of decoupling arterial tone to the ratio of the natural arterial anxiety of person under inspection of person under inspection comprise 1.01 or larger, 1.02 or larger, 1.03 or larger, 1.04 or larger, 1.05 or larger, 1.06 or larger, 1.07 or larger, 1.08 or larger, 1.09 or larger, 1.10 or larger, 1.15 or larger, 1.20 or larger, 1.30 or larger, 1.40 or larger and 1.50 or larger.When determining decoupling or high power situation, the method can warn user further.Such warning can be the notice or sound announced on a graphical user interface.

Figure 12 illustrates and implements at this describing method to monitor the primary clustering of person under inspection Ti Nei center to the system of peripheral decoupling.The method can be implemented in existing patient monitoring instrument part, or can be used as the enforcement of special monitor controller.As mentioned above, pressure or with some other input signals of proportional pressure can in fact in two ways in one or both sense: invasive and Noninvasive.For convenience, system is described to measure the arteriotony contrary with some other input signals changing pressure into.

Figure 12 is that integrity illustrates two kinds of pressure-sensing types.In the most realistic application of method described here, usually implement one or several change.In the invasive application of method described here, conventional pressure sensor 100 is arranged on conduit 110, and conduit 110 inserts in the tremulous pulse 120 of a part 130 for human or animal's patient body.Tremulous pulse 120 is any tremulous pulse in Arterial system, such as femoral artery, radial artery or brachial artery.In the Noninvasive application of method described here, conventional pressure sensor 200 such as photo-plethysmographic (photo-plethysmographic) blood pressure probe is outside in any usual manner to be installed, such as use the cuff around finger 230, or be arranged on the changer on patients wrist.Figure 12 schematically illustrates two types.

The signal being derived from sensor 100,200 is delivered to processing system 300 through any known adapter as input, and it comprises one or more processor and other support hardware and is usually included so that the systems soft ware (not shown) of processing signals and run time version.Method described here can use remodeling, the personal computer of standard is implemented, or during accessible site implements to larger special monitoring system.For using together with method described here, processing system 300 also can comprise or be connected to perform that normal signal Processing tasks such as amplifies as required, the modulate circuit 302 of filtering or range finding.Then input pressure signal P (t) that nurse one's health, sensing changes digital form into by conventional analog-digital converter ADC 304, and ADC 304 has its time reference or obtains its clock reference from clock circuit 305.As good understanding, the sample frequency of ADC 304 should be selected about Nyquist criterion, to avoid pressure signal distortion (this process is very famous in digital processing field).The output being derived from ADC 304 is discrete pressure signal P (k), and its value can be stored in conventional memory circuit (not shown).

Value P (k) is passed to memorizer by the software module 310 comprising computer-executable code or obtains from memorizer, to implement multivariate statistical model, thus determines decoupling and the hemodynamics arterial tone of person under inspection.The design of such software module 310 is flat-footed for computer programming those skilled in the art.

If used, so patient specific data such as age, height, body weight, BSA etc. are stored in memory areas 315, and this memory areas 315 also can store other predefined parameter, such as threshold value or threshold range value.Any known entering apparatus 400 can be used to input these values in a usual manner.

Completing more in module 320 of arterial tone.Computing module 320 comprises computer-executable code, and is considered as the input of module 310 output, then performs selected arterial tone and calculates.

As by Figure 12 diagram, result can be delivered to further module (330) so that additional treatments finally showing on conventional display or registering device 500, to present to user and to be understood by user.As entering apparatus 400, display 500 is usually same is the use of other object by processing system.

For each method of method described here, user can be notified when decoupling being detected.By announcing notice to notify user's decoupling on display 500 or another graphic user interface device.Further, sound can be used to notify user's decoupling.Both vision and audible signal can be used.

The block diagram of example embodiment reference method of the present invention, equipment and computer program describes above.Those skilled in the art understand the combination of square frame in each square frame of block diagram and block diagram respectively by comprising the various execution of instrument of computer program instructions.These computer program instructions can be loaded in general purpose computer, special-purpose computer or other programmable data processing device thus to produce machine, create to make the instruction performed on computer or other programmable data processing device the means implementing the function of specifying in the block.

Method described here relates to the computer program instructions that can be stored in computer-readable memory further, its bootable computer or other programmable data processing device, such as in processor or processing system, (be shown as 300 in fig. 12), thus run in a concrete fashion, to make the instruction be stored in computer-readable memory produce goods, it comprises computer-readable instruction to implement the function of specifying in diagrammatic block diagram in fig. 12.Computer program instructions also can be loaded in computer, processing system 300 or other programmable data processing device, thus cause sequence of operations step to be performed on computer, processing system 300 or other programmable device, thus produce computer-implemented process, with the step making the instruction performed on computer or other programmable device be provided for the appointed function implemented in the block.In addition, be used for performing various calculating and the various software modules 310,320 and 330 performing correlation technique step described here also can be stored as computer executable instructions on computer-readable media, to allow method to be loaded in different disposal system and to be performed by different disposal system.

Therefore, the square frame support of block diagram performs the combination of the means of specific function, the combination of the step of execution specific function and the program instruction means of execution specific function.Those skilled in the art understand the combination of square frame in each square frame of block diagram and block diagram by performing special the implementing based on hardware system of specific function or step, or are implemented by the combination of specialized hardware and computer instruction.

Scope is by the restriction of the embodiment of disclosed herein, a small amount of aspect of meant for illustration the present invention, and any embodiment of functional equivalent all falls in category of the present invention.Except to illustrate and except method described here, various method remodeling is obvious for those skilled in the art, and intention falls in the category of claim.Further, although only some representative combination of method step disclosed herein is specifically discussed in the above embodiments, other combination of method step is obvious for those skilled in the art, and also intention falls in the category of claim.Therefore the one combination of step is clearly referred at this; But, even without clearly stating, also comprise other combination of step.Term " comprises " and to change and term " comprises " and changes synonym as used herein, and is open, unrestriced term.

Claims (31)

1. monitor person under inspection Ti Nei center to a method for peripheral arterial pressure decoupling, comprise:

The arterial pressure waveform data coming from described person under inspection are provided;

To described arterial pressure waveform market demand first multivariate statistical model, thus determine first arterial tone of described person under inspection, described first multivariate statistical model obtains from being derived from experience center to one group of arterial pressure waveform data preparation of a group test person under inspection of peripheral arterial pressure decoupling, and described first multivariate statistical model provides the value of first arterial tone of described person under inspection;

To described arterial pressure waveform market demand second multivariate statistical model, thus determine second arterial tone of described person under inspection, described second multivariate statistical model is prepared from one group of arterial pressure waveform data of a group test person under inspection with normal hemodynamic situation and obtains, and described second multivariate statistical model provides the value of second arterial tone of described person under inspection; And

First arterial tone of more described person under inspection and second arterial tone of described person under inspection,

The difference that generation between first arterial tone of wherein said person under inspection and second arterial tone of described person under inspection is greater than the numerical value fiducial value of threshold value represents that described person under inspection experiences center to peripheral arterial pressure decoupling.

2. method according to claim 1, comprise further and use first arterial tone of described person under inspection and second arterial tone of described person under inspection to the second cardiac output of the first cardiac output and person under inspection that calculate person under inspection, the difference between first cardiac output of wherein said person under inspection and second cardiac output of described person under inspection is greater than threshold value and represents that described person under inspection experiences center to peripheral arterial pressure decoupling.

3. method according to claim 1, difference between first arterial tone of wherein said person under inspection and second arterial tone of described person under inspection produces numerical value fiducial value, and described numerical value fiducial value is calculated as the percentage change of first arterial tone of person under inspection described compared with second arterial tone of described person under inspection.

4. method according to claim 1, wherein said first and second multivariate statistical model are based on the one group of factor comprising the one or more parameters affected by blood vessel situation.

5. method according to claim 1, wherein said first and second multivariate statistical model are based on the one group of factor comprising one or more parameter, this one or more parameter is selected from the set that parameter is formed below: (a) is based on the parameter of the standard deviation of described arterial pressure waveform data, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection.

6. method according to claim 1, wherein said first and second multivariate statistical model are based on one group of factor, this group factor comprises: (a) is based on the parameter of the standard deviation of described arterial pressure waveform data, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection.

7. method according to claim 3, wherein said threshold value is 1%.

8. method according to claim 3, wherein said threshold value is 5%.

9. method according to claim 3, wherein said threshold value is 10%.

10. method according to claim 3, wherein said threshold value is 20%.

11. methods according to claim 1, wherein analyze the difference between first arterial tone of described person under inspection and second arterial tone of described person under inspection continuously.

12. methods according to claim 1, warn user when the difference comprised further between first arterial tone and second arterial tone of described person under inspection of described person under inspection is greater than described threshold value.

13. methods according to claim 12, wherein by announcing user described in notification alert on a graphical user interface.

14. methods according to claim 12, wherein warn described user by sounding.

15. methods according to claim 1, wherein use described first multivariate statistical model of step generation below to determine first arterial tone of described person under inspection by application:

Press the person under inspection of decoupling to determine approximating function from experience center to peripheral artery, about performance, described approximating function depends on that one group of the blood pressure parameter of the arterial tone clinical baseline measurements determined is depended in one group of the blood pressure parameter of the arterial tone clinical baseline measurements that obtains and performance, described approximating function is the parameter of function (a) based on the standard deviation of described arterial pressure waveform data of parameter at least below, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection,

One group of arterial blood pressure parameter is determined from described arterial blood pressure waveform data, described one group of arterial blood pressure parameter at least comprises (a) parameter based on the standard deviation of described arterial pressure waveform data, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection, and

By estimating described approximating function by described one group of arterial blood pressure parameter, estimate first arterial tone of described person under inspection.

16. methods according to claim 1, wherein use described second multivariate statistical model of step generation below to determine second arterial tone of described person under inspection by application:

Approximating function is determined from the person under inspection with normal hemodynamic situation, about performance, described approximating function depends on that one group of the blood pressure parameter of the arterial tone clinical baseline measurements determined is depended in one group of the blood pressure parameter of the arterial tone clinical baseline measurements that obtains and performance, described approximating function is the parameter of function (a) based on the standard deviation of described arterial pressure waveform data of parameter at least below, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection,

One group of arterial blood pressure parameter is determined from described arterial blood pressure waveform data, described one group of arterial blood pressure parameter at least comprises (a) parameter based on the standard deviation of described arterial pressure waveform data, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection, and

By estimating described approximating function by described one group of arterial blood pressure parameter, estimate second arterial tone of described person under inspection.

Monitor person under inspection Ti Nei center to the method for peripheral arterial pressure decoupling, comprise for 17. 1 kinds:

The arterial pressure waveform data coming from described person under inspection are provided;

To described arterial pressure waveform market demand first multivariate statistical model, thus determine first arterial tone of described person under inspection, described first multivariate statistical model obtains from being derived from experience center to one group of arterial pressure waveform data preparation of a group test person under inspection of peripheral arterial pressure decoupling, and described first multivariate statistical model provides the value of first arterial tone of described person under inspection;

To described arterial pressure waveform market demand second multivariate statistical model, thus determine second arterial tone of described person under inspection, described second multivariate statistical model is prepared from one group of arterial pressure waveform data of a group test person under inspection with normal hemodynamic situation and obtains, and described second multivariate statistical model provides the value of second arterial tone of described person under inspection; And

First arterial tone of more described person under inspection and second arterial tone of described person under inspection,

The ratio of the first arterial tone to second arterial tone of described person under inspection of wherein said person under inspection is greater than threshold value than representing that described person under inspection experiences center to peripheral arterial pressure decoupling.

18. methods according to claim 17, comprise further and use first arterial tone of described person under inspection and second arterial tone of described person under inspection to the second cardiac output of the first cardiac output and person under inspection that calculate person under inspection, the ratio between first cardiac output of wherein said person under inspection and second cardiac output of described person under inspection is greater than threshold value and represents that described person under inspection experiences center to peripheral arterial pressure decoupling.

19. methods according to claim 17, wherein said first and second multivariate statistical model are based on the one group of factor comprising the one or more parameters affected by blood vessel situation.

20. methods according to claim 17, wherein said first and second multivariate statistical model are based on the one group of factor comprising one or more parameter, this one or more parameter is selected from the set that parameter is formed below: (a) is based on the parameter of the standard deviation of described arterial pressure waveform data, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection.

21. methods according to claim 17, wherein said first and second multivariate statistical model are based on one group of factor, this group factor comprises: (a) is based on the parameter of the standard deviation of described arterial pressure waveform data, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection.

22. methods according to claim 17, wherein said threshold value ratio is 1.01.

23. methods according to claim 17, wherein said threshold value ratio is 1.05.

24. methods according to claim 17, wherein said threshold value ratio is 1.10.

25. methods according to claim 17, wherein said threshold value ratio is 1.20.

26. methods according to claim 17, wherein analyze the ratio between first arterial tone of described person under inspection and second arterial tone of described person under inspection continuously.

27. methods according to claim 17, the ratio of the first arterial tone to second arterial tone of described person under inspection comprised further as described person under inspection be greater than described threshold value than time warning user.

28. methods according to claim 27, wherein by announcing user described in notification alert on a graphical user interface.

29. methods according to claim 27, wherein warn described user by sounding.

30. methods according to claim 17, wherein use described first multivariate statistical model of step generation below to determine first arterial tone of described person under inspection by application:

Press the person under inspection of decoupling to determine approximating function from experience center to peripheral artery, about performance, described approximating function depends on that one group of the blood pressure parameter of the arterial tone clinical baseline measurements determined is depended in one group of the blood pressure parameter of the arterial tone clinical baseline measurements that obtains and performance, described approximating function is the parameter of function (a) based on the standard deviation of described arterial pressure waveform data of parameter at least below, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection,

One group of arterial blood pressure parameter is determined from described arterial blood pressure waveform data, described one group of arterial blood pressure parameter at least comprises (a) parameter based on the standard deviation of described arterial pressure waveform data, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection, and

By estimating described approximating function by described one group of arterial blood pressure parameter, estimate first arterial tone of described person under inspection.

31. methods according to claim 17, wherein use described second multivariate statistical model of step generation below to determine second arterial tone of described person under inspection by application:

Approximating function is determined from the person under inspection with normal hemodynamic situation, about performance, described approximating function depends on that one group of the blood pressure parameter of the arterial tone clinical baseline measurements determined is depended in one group of the blood pressure parameter of the arterial tone clinical baseline measurements that obtains and performance, described approximating function is the parameter of function (a) based on the standard deviation of described arterial pressure waveform data of parameter at least below, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection,

One group of arterial blood pressure parameter is determined from described arterial blood pressure waveform data, described one group of arterial blood pressure parameter at least comprises (a) parameter based on the standard deviation of described arterial pressure waveform data, b () is based on the parameter of the heart rate of described person under inspection, c () is based on the parameter of area below the constriction of arterial blood pressure signal, d () is based on the parameter of duration of contraction, e () is based on the parameter of described duration of contraction to the ratio of diastole persistent period, f () is based on the parameter of the mean arterial pressure of one group of arterial pressure waveform data, g () is based on the parameter of the pressure weighting standard difference of one group of arterial pressure waveform data, h () is based on the parameter of the pressure weighted mean of one group of arterial pressure waveform data, i () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of degree of bias value, j () is beated based on the arterial pulse of one group of arterial pressure waveform data the parameter of kurtosis value, k () is based on the parameter of the pressure weighting degree of bias of one group of arterial pressure waveform data, l () is based on the parameter of the pressure weighting kurtosis of one group of arterial pressure waveform data, m () is based on the parameter of the pressure-dependent Windkessel compliance of one group of arterial pressure waveform data, and (n) is based on the parameter of the body surface area of described person under inspection, and

By estimating described approximating function by described one group of arterial blood pressure parameter, estimate second arterial tone of described person under inspection.